Neo4j to FalkorDB migration (without APOC)

Migrating graph database contents from one property graph solution to another is a common task.
In this example, we use stardard cypher queries to extract data from a sample graph example availble in neo4j namely "Movies",
allow transformation of labels and properties, and as a next step load the csv files one by one into falkor db.
Please note that you can use any source neo4j dataset, and the process migrates nodes, edges, constraints and indexes.

Step 1: Setting up neo4j

Follow neo4j documentation to setup a locally run neo4j database: https://neo4j.com/docs/operations-manual/current/installation/

Step 2: Loading the movies sample graph data to neo4j

You can load the movies dataset by following the ":guide movies" command in the neo4j browser. More details are available here: https://neo4j.com/docs/getting-started/appendix/example-data/

Step 3: Reviewing and updating mapping configuration file

The configuration file migrate_config.json allows you to modify how the labels and properties are represented in FalkorDB. In order to extract the ontology (for a dataset which is not the movies sample dataset),
you may generate a template config file using this command:

python3 neo4j_to_csv_extractor.py --password <your-neo4j-password> --generate-template <your-template>.json --analyze-only

Sample output of the ontology extraction phase is shown below as executed against the movies dataset

Connecting to Neo4j at bolt://localhost:7687 with username 'neo4j'...
✅ Loaded configuration from migrate_config.json
✅ Successfully connected to Neo4j!
🔍 Analyzing Neo4j database topology...
  Found 2 node labels: ['Movie', 'Person']
  Found 6 relationship types: ['ACTED_IN', 'DIRECTED', 'PRODUCED', 'WROTE', 'FOLLOWS', 'REVIEWED']

📊 Analyzing node properties...
  Analyzing Movie properties...
    Found 3 properties: ['released', 'tagline', 'title']
  Analyzing Person properties...
    Found 2 properties: ['born', 'name']

🔗 Analyzing relationship properties...
  Analyzing ACTED_IN properties...
    Found 1 properties: ['roles']
  Analyzing DIRECTED properties...
    No properties found for DIRECTED
  Analyzing FOLLOWS properties...
    No properties found for FOLLOWS
  Analyzing PRODUCED properties...
    No properties found for PRODUCED
  Analyzing REVIEWED properties...
    Found 2 properties: ['rating', 'summary']
  Analyzing WROTE properties...
    No properties found for WROTE

📈 Gathering database statistics...
  Movie: 38 nodes
  Person: 133 nodes
  ACTED_IN: 172 relationships
  DIRECTED: 44 relationships
  PRODUCED: 15 relationships
  WROTE: 10 relationships
  FOLLOWS: 3 relationships
  REVIEWED: 9 relationships

✅ Generated template migration config: shahar_template.json
📝 Total elements analyzed:
   - 2 node labels
   - 6 relationship types
   - 5 node properties
   - 3 relationship properties

📋 Analysis complete! Use the generated template to customize your migration.

Step 4: Extracting data from neo4j and generating csv files

To extract data from neo4j you can activate the relevant python script:

python3 neo4j_to_csv_extractor.py --password <your-neo4j-password> --config migrate_config.json

The script would read data from neo4j and create in csv_output subfolder all nodes and edges csv files with headers and content adapted based on guidelines in the migrate_config.json file

Execution output example

Connecting to Neo4j at bolt://localhost:7687 with username 'neo4j'...
✅ Loaded configuration from migrate_config.json
✅ Successfully connected to Neo4j!
Starting Neo4j to CSV extraction...
Found 2 node labels: ['Movie', 'Person']
Extracting nodes with label: Movie
  🔄 Transformed Movie.released: '1999' -> '1999'
  🔄 Transformed Movie.released: '2003' -> '2003'
  🔄 Transformed Movie.released: '2003' -> '2003'
  🔄 Transformed Movie.released: '1997' -> '1997'
  🔄 Transformed Movie.released: '1992' -> '1992'
  🔄 Transformed Movie.released: '1986' -> '1986'
  🔄 Transformed Movie.released: '2000' -> '2000'
  🔄 Transformed Movie.released: '1986' -> '1986'
  🔄 Transformed Movie.released: '1997' -> '1997'
  🔄 Transformed Movie.released: '1998' -> '1998'
  🔄 Transformed Movie.released: '1999' -> '1999'
  🔄 Transformed Movie.released: '1998' -> '1998'
  🔄 Transformed Movie.released: '1993' -> '1993'
  🔄 Transformed Movie.released: '1990' -> '1990'
  🔄 Transformed Movie.released: '1998' -> '1998'
  🔄 Transformed Movie.released: '1996' -> '1996'
  🔄 Transformed Movie.released: '2000' -> '2000'
  🔄 Transformed Movie.released: '2006' -> '2006'
  🔄 Transformed Movie.released: '1996' -> '1996'
  🔄 Transformed Movie.released: '1992' -> '1992'
  🔄 Transformed Movie.released: '1995' -> '1995'
  🔄 Transformed Movie.released: '2012' -> '2012'
  🔄 Transformed Movie.released: '2006' -> '2006'
  🔄 Transformed Movie.released: '2006' -> '2006'
  🔄 Transformed Movie.released: '2008' -> '2008'
  🔄 Transformed Movie.released: '2009' -> '2009'
  🔄 Transformed Movie.released: '1999' -> '1999'
  🔄 Transformed Movie.released: '2008' -> '2008'
  🔄 Transformed Movie.released: '1992' -> '1992'
  🔄 Transformed Movie.released: '1995' -> '1995'
  🔄 Transformed Movie.released: '1996' -> '1996'
  🔄 Transformed Movie.released: '2000' -> '2000'
  🔄 Transformed Movie.released: '1975' -> '1975'
  🔄 Transformed Movie.released: '2003' -> '2003'
  🔄 Transformed Movie.released: '1999' -> '1999'
  🔄 Transformed Movie.released: '2007' -> '2007'
  🔄 Transformed Movie.released: '2004' -> '2004'
  🔄 Transformed Movie.released: '1992' -> '1992'
  Processed 38 nodes...
  Exported 38 nodes to csv_output/nodes_movie.csv
Extracting nodes with label: Person
  Processed 133 nodes...
  Exported 133 nodes to csv_output/nodes_person.csv
Found 6 relationship types: ['ACTED_IN', 'DIRECTED', 'PRODUCED', 'WROTE', 'FOLLOWS', 'REVIEWED']
Extracting relationships of type: ACTED_IN
  Processed 172 relationships...
  Exported 172 relationships to csv_output/edges_acted_in.csv
Extracting relationships of type: DIRECTED
  Processed 44 relationships...
  Exported 44 relationships to csv_output/edges_directed.csv
Extracting relationships of type: PRODUCED
  Processed 15 relationships...
  Exported 15 relationships to csv_output/edges_produced.csv
Extracting relationships of type: WROTE
  Processed 10 relationships...
  Exported 10 relationships to csv_output/edges_wrote.csv
Extracting relationships of type: FOLLOWS
  Processed 3 relationships...
  Exported 3 relationships to csv_output/edges_follows.csv
Extracting relationships of type: REVIEWED
  Processed 9 relationships...
  Exported 9 relationships to csv_output/edges_reviewed.csv
Extracting database indexes...
  Exported 4 indexes to csv_output/indexes.csv
Extracting database constraints...
  Exported 2 constraints to csv_output/constraints.csv
Generated FalkorDB load script: csv_output/load_to_falkordb.cypher
Generated FalkorDB index creation script: csv_output/create_indexes_falkordb.cypher

Extraction complete!
Node files: 2
Edge files: 6
Index file: csv_output/indexes.csv
Constraint file: csv_output/constraints.csv
Data load script: csv_output/load_to_falkordb.cypher
Index creation script: csv_output/create_indexes_falkordb.cypher

Step 5: Loading csv data into falkordb

Setup falkordb on your local machine (you can either select the "server" option only,
or use standard deployment that includes the browser component: https://docs.falkordb.com/getting_started.html After setup you will have falkordb data conntection at port 6379 and web browser at port 3000

python3 falkordb_csv_loader.py MOVIES --port 6379 --stats

Note: In case your FalkorDB connection is secured with username and password you can add them according to the syntax described below. You may also control the batch size used per loaded CSV file.

usage: falkordb_csv_loader.py [-h] [--host HOST] [--port PORT] [--username USERNAME] [--password PASSWORD] [--batch-size BATCH_SIZE] [--stats] [--csv-dir CSV_DIR]
                              [--merge-mode]
                              graph_name

Load CSV files into FalkorDB

positional arguments:
  graph_name            Target graph name in FalkorDB

options:
  -h, --help            show this help message and exit
  --host HOST           FalkorDB host
  --port PORT           FalkorDB port
  --username USERNAME   FalkorDB username (optional)
  --password PASSWORD   FalkorDB password (optional)
  --batch-size BATCH_SIZE
                        Batch size for loading (default: 5000)
  --stats               Show graph statistics after loading
  --csv-dir CSV_DIR     Directory containing CSV files (default: csv_output)
  --merge-mode          Use MERGE instead of CREATE for upsert behavior

Execution output example

Connecting to FalkorDB at localhost:6379...
✅ Connected to FalkorDB graph 'MOVIES'
Found 2 node files and 6 edge files

🗼️ Setting up database schema...
🔧 Creating ID indexes for all node labels...
  Creating ID index: CREATE INDEX ON :Movie(id)
  Creating ID index: CREATE INDEX ON :Person(id)
✅ Created 2 ID indexes
🔧 Creating indexes from CSV...
  Read 4 rows from csv_output/indexes.csv
  Creating: CREATE INDEX ON :Movie(name)
  Creating: CREATE INDEX ON :Person(name)
✅ Created 2 indexes from CSV, skipped 2
🔧 Creating supporting indexes for constraints...
  Read 2 rows from csv_output/constraints.csv
  Creating supporting index: CREATE INDEX FOR (n:Movie) ON (n.name)
  Creating supporting index: CREATE INDEX FOR (n:Person) ON (n.name)
🔒 Creating constraints...
  Read 2 rows from csv_output/constraints.csv
  ✅ Successfully created UNIQUE constraint on Movie(name), status: PENDING
  ✅ Successfully created UNIQUE constraint on Person(name), status: PENDING
✅ Created 2 constraints

[2025-08-03 14:04:06] 📥 Loading nodes...
[2025-08-03 14:04:06] Loading nodes from csv_output/nodes_movie.csv...
  Read 38 rows from csv_output/nodes_movie.csv
  CSV headers: ['id', 'labels', 'release_year', 'tagline', 'name']
    Record 1: properties = {'release_year': 1999, 'tagline': 'Welcome to the Real World', 'name': 'The Matrix'}
    Generated query: CREATE (:Movie {id: 0, release_year: 1999, tagline: 'Welcome to the Real World', name: 'The Matrix'})
    Record 2: properties = {'release_year': 2003, 'tagline': 'Free your mind', 'name': 'The Matrix Reloaded'}
    Generated query: CREATE (:Movie {id: 9, release_year: 2003, tagline: 'Free your mind', name: 'The Matrix Reloaded'})
    Record 3: properties = {'release_year': 2003, 'tagline': 'Everything that has a beginning has an end', 'name': 'The Matrix Revolutions'}
    Generated query: CREATE (:Movie {id: 10, release_year: 2003, tagline: 'Everything that has a beginning has an end', name: 'The Matrix Revolutions'})
[2025-08-03 14:04:06] Batch complete: Loaded 38 nodes (Duration: 0:00:00.012957)
[2025-08-03 14:04:06] ✅ Loaded 38 Movie nodes (Duration: 0:00:00.013085)
[2025-08-03 14:04:06] Loading nodes from csv_output/nodes_person.csv...
  Read 133 rows from csv_output/nodes_person.csv
  CSV headers: ['id', 'labels', 'birth_year', 'name']
    Record 1: properties = {'birth_year': 1964, 'name': 'Keanu Reeves'}
    Generated query: CREATE (:Person {id: 1, birth_year: 1964, name: 'Keanu Reeves'})
    Record 2: properties = {'birth_year': 1967, 'name': 'Carrie-Anne Moss'}
    Generated query: CREATE (:Person {id: 2, birth_year: 1967, name: 'Carrie-Anne Moss'})
    Record 3: properties = {'birth_year': 1961, 'name': 'Laurence Fishburne'}
    Generated query: CREATE (:Person {id: 3, birth_year: 1961, name: 'Laurence Fishburne'})
[2025-08-03 14:04:06] Batch complete: Loaded 133 nodes (Duration: 0:00:00.035237)
[2025-08-03 14:04:06] ✅ Loaded 133 Person nodes (Duration: 0:00:00.035437)
[2025-08-03 14:04:06] ✅ All nodes loaded (Total duration: 0:00:00.048588)

[2025-08-03 14:04:06] 🔗 Loading edges...
[2025-08-03 14:04:06] Loading edges from csv_output/edges_produced.csv...
  Read 15 rows from csv_output/edges_produced.csv
    Record 1: source_label=, target_label=
    Using CREATE mode for relationships
    Record 2: source_label=, target_label=
    Using CREATE mode for relationships
    Record 3: source_label=, target_label=
    Using CREATE mode for relationships
[2025-08-03 14:04:06] Batch complete: Loaded 15 edges (Duration: 0:00:00.005070)
[2025-08-03 14:04:06] ✅ Loaded 15 PRODUCED relationships (Duration: 0:00:00.005181)
[2025-08-03 14:04:06] Loading edges from csv_output/edges_follows.csv...
  Read 3 rows from csv_output/edges_follows.csv
    Record 1: source_label=, target_label=
    Using CREATE mode for relationships
    Record 2: source_label=, target_label=
    Using CREATE mode for relationships
    Record 3: source_label=, target_label=
    Using CREATE mode for relationships
[2025-08-03 14:04:06] Batch complete: Loaded 3 edges (Duration: 0:00:00.000969)
[2025-08-03 14:04:06] ✅ Loaded 3 FOLLOWS relationships (Duration: 0:00:00.001039)
[2025-08-03 14:04:06] Loading edges from csv_output/edges_acted_in.csv...
  Read 172 rows from csv_output/edges_acted_in.csv
    Record 1: source_label=, target_label=
    Using CREATE mode for relationships
    Record 2: source_label=, target_label=
    Using CREATE mode for relationships
    Record 3: source_label=, target_label=
    Using CREATE mode for relationships
[2025-08-03 14:04:06] Batch complete: Loaded 172 edges (Duration: 0:00:00.066439)
[2025-08-03 14:04:06] ✅ Loaded 172 ACTED_IN relationships (Duration: 0:00:00.068332)
[2025-08-03 14:04:06] Loading edges from csv_output/edges_reviewed.csv...
  Read 9 rows from csv_output/edges_reviewed.csv
    Record 1: source_label=, target_label=
    Using CREATE mode for relationships
    Record 2: source_label=, target_label=
    Using CREATE mode for relationships
    Record 3: source_label=, target_label=
    Using CREATE mode for relationships
[2025-08-03 14:04:06] Batch complete: Loaded 9 edges (Duration: 0:00:00.003290)
[2025-08-03 14:04:06] ✅ Loaded 9 REVIEWED relationships (Duration: 0:00:00.003417)
[2025-08-03 14:04:06] Loading edges from csv_output/edges_wrote.csv...
  Read 10 rows from csv_output/edges_wrote.csv
    Record 1: source_label=, target_label=
    Using CREATE mode for relationships
    Record 2: source_label=, target_label=
    Using CREATE mode for relationships
    Record 3: source_label=, target_label=
    Using CREATE mode for relationships
[2025-08-03 14:04:06] Batch complete: Loaded 10 edges (Duration: 0:00:00.003456)
[2025-08-03 14:04:06] ✅ Loaded 10 WROTE relationships (Duration: 0:00:00.003566)
[2025-08-03 14:04:06] Loading edges from csv_output/edges_directed.csv...
  Read 44 rows from csv_output/edges_directed.csv
    Record 1: source_label=, target_label=
    Using CREATE mode for relationships
    Record 2: source_label=, target_label=
    Using CREATE mode for relationships
    Record 3: source_label=, target_label=
    Using CREATE mode for relationships
[2025-08-03 14:04:06] Batch complete: Loaded 44 edges (Duration: 0:00:00.014205)
[2025-08-03 14:04:06] ✅ Loaded 44 DIRECTED relationships (Duration: 0:00:00.014337)
[2025-08-03 14:04:06] ✅ All edges loaded (Total duration: 0:00:00.095961)

[2025-08-03 14:04:06] ✅ Successfully loaded data into graph 'MOVIES' (Total loading time: 0:00:00.144561)

📊 Graph Statistics:
Nodes:
  ['Movie']: 38
  ['Person']: 133
Relationships:
  PRODUCED: 15
  REVIEWED: 9
  DIRECTED: 44
  WROTE: 10
  ACTED_IN: 172
  FOLLOWS: 3

🔍 Sample Person nodes with their attributes:
  Node 1: (:Person{birth_year:1964,id:1,name:"Keanu Reeves"})
  Node 2: (:Person{birth_year:1967,id:2,name:"Carrie-Anne Moss"})
  Node 3: (:Person{birth_year:1961,id:3,name:"Laurence Fishburne"})

Step 6: Validating content of data in FalkorDB

Here is how the same cypher query is visualized in both Neo4j and FalkorDB broswers:

MATCH p=()-[:REVIEWED]->() RETURN p LIMIT 25;

Neo4j view

FalkorDB view

Good Luck!

Name		Name	Last commit message	Last commit date
Latest commit History 31 Commits
csv_output		csv_output
README.md		README.md
falkordb_csv_loader.py		falkordb_csv_loader.py
migrate_config.json		migrate_config.json
neo4j_to_csv_extractor.py		neo4j_to_csv_extractor.py
requirements.txt		requirements.txt

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Neo4j to FalkorDB migration (without APOC)

Step 1: Setting up neo4j

Step 2: Loading the movies sample graph data to neo4j

Step 3: Reviewing and updating mapping configuration file

Sample output of the ontology extraction phase is shown below as executed against the movies dataset

Step 4: Extracting data from neo4j and generating csv files

Execution output example

Step 5: Loading csv data into falkordb

Execution output example

Step 6: Validating content of data in FalkorDB

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Neo4j to FalkorDB migration (without APOC)

Step 1: Setting up neo4j

Step 2: Loading the movies sample graph data to neo4j

Step 3: Reviewing and updating mapping configuration file

Sample output of the ontology extraction phase is shown below as executed against the movies dataset

Step 4: Extracting data from neo4j and generating csv files

Execution output example

Step 5: Loading csv data into falkordb

Execution output example

Step 6: Validating content of data in FalkorDB

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